Intelligent fault diagnosis of rolling bearing using deep wavelet auto-encoder with extreme learning machine

Shao, Haidong; Jiang, Hongkai; Li, Xingqiu; Wu, Shuaipeng

doi:10.1016/j.knosys.2017.10.024

Cited by 264 publications

(92 citation statements)

References 34 publications

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“…Experiment 1 (the test bearing data set of Western Reserve University). In order to verify the superiority of the proposed method, the XCN model proposed in the previous section is compared with other deep learning algorithms for nearly three years: DWAE+ELM [23] which is based on deep wavelet autoencoder with extreme learning machine, CapsNet which is based on standard capsule neural network, MPE+ISVM+BT [22] which is based on multiscale permutation entropy and improved support vector machine based binary tree, AE+ES+CNN [26] which is based on an acoustic emission analysis-based bearing fault diagnosis invariant under fluctuations of rotational speeds using envelope spectrums and a convolutional neural network, and DBN [25] which is based on the standard deep belief network. Finally, the test accuracy of each algorithm is shown in Table 2 and Figure 15.…”

Section: Reliably Of the Xcn Modelmentioning

confidence: 99%

“…Secondly, the timefrequency graphs were imported into the XCN model after adjusting the pixel size. Finally, the same input was imported into other algorithms such as DWAE+ELM [23], CapsNet, MPE+ISVM+BT [22], AE+ES+CNN [26], and DBN [25] and tested the failure diagnosis performance of these methods. In this experiment, sixty percent of the adjusted timefrequency graphs were used for algorithm training and forty percent were used for testing.…”

Section: Reliably Of the Xcn Modelmentioning

confidence: 99%

“…In addition, there are other ways, such as improved ensemble local mean decomposition (IELMD) [16,17], kernel regression residual [18], and modified variable modal decomposition (MVMD) [19,20]. Methods based on big data and machine learning or 2 Complexity deep learning include support vector machine (SVM) [21,22], extreme learning machine (ELM) [23], kernel extreme learning machine (KELM) [24], deep belief network (DBN) [25], and convolutional neural network (CNN) [26,27]. In general, these methods can solve most classification problems well.…”

Section: Introductionmentioning

confidence: 99%

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A Novel Method for Intelligent Fault Diagnosis of Bearing Based on Capsule Neural Network

Wang

Zheng

et al. 2019

Complexity

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In the era of big data, data-driven methods mainly based on deep learning have been widely used in the field of intelligent fault diagnosis. Traditional neural networks tend to be more subjective when classifying fault time-frequency graphs, such as pooling layer, and ignore the location relationship of features. The newly proposed neural network named capsules network takes into account the size and location of the image. Inspired by this, capsules network combined with the Xception module (XCN) is applied in intelligent fault diagnosis, so as to improve the classification accuracy of intelligent fault diagnosis. Firstly, the fault time-frequency graphs are obtained by wavelet time-frequency analysis. Then the time-frequency graphs data which are adjusted the pixel size are input into XCN for training. In order to accelerate the learning rate, the parameters which have bigger change are punished by cost function in the process of training. After the operation of dynamic routing, the length of the capsule is used to classify the types of faults and get the classification of loss. Then the longest capsule is used to reconstruct fault time-frequency graphs which are used to measure the reconstruction of loss. In order to determine the convergence condition, the three losses are combined through the weight coefficient. Finally, the proposed model and the traditional methods are, respectively, trained and tested under laboratory conditions and actual wind turbine gearbox conditions to verify the classification ability and reliable ability.

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Section: Reliably Of the Xcn Modelmentioning

confidence: 99%

Section: Reliably Of the Xcn Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Novel Method for Intelligent Fault Diagnosis of Bearing Based on Capsule Neural Network

Wang

Zheng

et al. 2019

Complexity

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“…The accuracy achieved more than 99% [5]. The detection of rolling bearing fault are used Continuous Deep Belief Network(CDBN) and Continuous Restricted Boltzmann machines(CRBMs) .The CDBN are optimized with genetic algorithm(GA) [6].The four condition of detection and classification in roller bearing: healthy, inner race defect, outer race defect and double holes in outer race used an Artificial Neural Network (ANN) [7].Fault diagnosis of rolling bearing are analysis by Hilbert Transform(HT) and Fast Fourier Transform(FFT).Artificial Neural Network used genetic algorithm into optimization [8]. In addition, the model of linear bearing was employed to detect fault form vibration signal.…”

Section: Fig1 Auto Core Adhesion Mounting Machine (Acam)mentioning

confidence: 99%

Linear Bearing Fault Detection in Operational Condition Using Artificial Neural Network

2019

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Nowadays, Factors of a competition of Hard Disk Drive (HDD) industry have reduced the cost of manufacturing process via increasing the rate of productivity and reliability of the automation machine. This paper aims to increase the efficacy of Condition-Based Maintenance (CBM) of linear bearing in Auto Core Adhesion Mounting machine (ACAM). The linear bearing faults considered in three causes such as healthy bearing, one ball bearing damage and one ball bearing damage with starved lubricant. The Fast Fourier Transform spectrum (FFT spectrum) can be detected for linear bearing faults and Artificial Neural Network (ANN) method used to analyze the cause of linear bearing faults in operational condition. The experimental results show the potential application of ANN and FFT spectrum technique as Fault Detection and Isolation (FDI) tool for linear bearing fault detection performance. The accuracy and decision making of ANN is enough to develop the diagnostic method for automation machine in operational condition.

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“…The industries vital challenge is the initial as well as the exposure and analysis of process faults to maintain a secure process and reduce the productivity loss. With the help of process monitoring these issues are addressed [10]. Into three groups the process monitoring approaches can be grouped they are model-based, knowledge-based and databased methods.…”

Section: Introductionmentioning

confidence: 99%

Fault Detection using Unsupervised Feature Learning on Big Data Based on Improved Weighted SoftMax Regression with Jaya Optimization

Keshari¹,

Krishna²

2019

IJITEE

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Intelligent fault detection is promising to deal with big data due to its ability in rapidly and efficiently processing collected signals and providing accurate detection results. In traditional fault detection methods, however, the features are manually extracted depending on prior knowledge and diagnostic expertise, such processes take advantage of human ingenuity but are time-consuming. Inspired by the idea of unsupervised feature learning artificial intelligence techniques are used to learn features from the raw data.As the dimensionality increases, the accuracy of fault identification methods implemented on big data decreases significantly. For supervised learning, large volume of data is needed which leads to high cost and time consuming. In this paper, an unsupervised learning approach is proposed on the basis of weighted softmax regression for fault detection using the power signals. In the proposed approach, the features are extracted from the unlabelled data. The developed approach is based on squirrel search algorithm and voting based weighted softmax regression with jaya optimization algorithm (IWSRJO_SSA). The proposed approach is simple and easy to carry out, though it attains high accuracy when compared to that of more advanced techniques. The features from the signals are extracted normally and some of the best features are selected using the squirrel search algorithm (SSA). Because of selecting the best features and using the improved weighted softmax regression, the proposed method achieves high accuracy in fault detection. Experiment on the power signal dataset shows the applicability of the proposed approach in fault detection on big data. The proposed system will be applicable on medical field, industrial field, electrical field and so on. The experimental results prove that the proposed method attains high accuracy and is superior to the other advanced methods.

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Intelligent fault diagnosis of rolling bearing using deep wavelet auto-encoder with extreme learning machine

Cited by 264 publications

References 34 publications

A Novel Method for Intelligent Fault Diagnosis of Bearing Based on Capsule Neural Network

A Novel Method for Intelligent Fault Diagnosis of Bearing Based on Capsule Neural Network

Linear Bearing Fault Detection in Operational Condition Using Artificial Neural Network

Fault Detection using Unsupervised Feature Learning on Big Data Based on Improved Weighted SoftMax Regression with Jaya Optimization

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